The challenge of visualizing and interacting with a massive spatial or multidimensional dataset exists in many problem domains ranging from hotspots in chip design to stresses on the hull of a ship to displaying humungous matrices. Spatial data can grow along its physical dimensions or it can grow in terms of sheer density. Interactive visualization that is trivial to do with a small dataset becomes unwieldy if the data size is excessive.
A multitude of problem domains, like chip design, deal with massive spatial data. Design elements such as instances, transistor geometries, and other electrical elements are often projected on a Euclidean plane. Frequently there is a need to visualize such data to assist a human analyst. A good visual debugging tool provides an overview of phenomena to a user. The user can drill down on demand. Typically, the lifecycle of generating any visual involves devising a geometric model representing shapes in a virtual scene, using software graphics libraries to transfer the geometric model to a digital image, and using image processing to enhance the image further. Image processing is computationally expensive and hence done for visuals that are fairly static. Image processing traditionally is infeasible for dynamic images that change often, such as with a visual debugger that is able to almost instantaneously refresh.